library(papaja)
## Loading required package: tinylabels
library(tinylabels)
library(ds4ling)
##
## ds4ling loaded
## Happy coding!
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2
## ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(untidydata)
library(here)
## here() starts at /Users/alejandrojaume/Desktop/stats_paper
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
##
## The following object is masked from 'package:dplyr':
##
## recode
##
## The following object is masked from 'package:purrr':
##
## some
my_data <- read_csv(here("data", "data_raw_proyecto.csv"))
## New names:
## Rows: 180 Columns: 14
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): sex, conservatorio dbl (10): participant, c_nat, c_soc, ef_fi, leng_cas,
## leng_cat, leng_est, ma... lgl (2): ...13, ...14
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...13`
## • `` -> `...14`
view(my_data)
glimpse(my_data)
## Rows: 180
## Columns: 14
## $ participant <dbl> 1, 2, 3, 4, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ sex <chr> "male", "male", "male", "female", "female", "male", "fem…
## $ conservatorio <chr> "no", "no", "no", "no", "no", "no", "no", "no", "no", "n…
## $ c_nat <dbl> 9, 8, 8, 8, 3, 8, 9, 4, 5, 5, 5, 8, 9, 7, 8, 5, 7, 5, 5,…
## $ c_soc <dbl> 9, 8, 10, 8, 3, 9, 9, 5, 7, 6, 6, 10, 10, 8, 10, 7, 8, 5…
## $ ef_fi <dbl> 9, 9, 9, 9, 9, 9, 8, 8, 6, 9, 9, 9, 10, 9, 10, 10, 7, 7,…
## $ leng_cas <dbl> 9, 7, 7, 7, 4, 9, 8, 5, 6, 5, 6, 7, 10, 8, 8, 6, 8, 6, 6…
## $ leng_cat <dbl> 8, 7, 7, 6, 5, 9, 8, 5, 6, 6, 4, 8, 10, 6, 6, 5, 6, 6, 6…
## $ leng_est <dbl> 9, 10, 9, 5, 3, 10, 10, 6, 5, 7, 7, 9, 10, 9, 9, 8, 9, 8…
## $ mat <dbl> 10, 6, 5, 6, 4, 7, 9, 3, 3, 6, 4, 6, 10, 5, 7, 1, 5, 4, …
## $ plast <dbl> 10, 8, 8, 6, 4, 9, 9, 6, 6, 10, 7, 8, 10, 7, 8, 7, 9, 5,…
## $ tecn <dbl> 9, 7, 7, 7, 3, 9, 9, 5, 5, 7, 6, 7, 9, 6, 7, 5, 6, 4, 5,…
## $ ...13 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ ...14 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
my_data_tidy <- select(my_data, participant, sex, conservatorio, c_nat, c_soc, ef_fi, leng_cas, leng_cat, leng_est, mat, plast, tecn)
my_data_tidy_proyecto <- my_data_tidy %>%
write_csv(., path = "data/data_tidy_proyecto.csv")
## Warning: The `path` argument of `write_csv()` is deprecated as of readr 1.4.0.
## ℹ Please use the `file` argument instead.
summary(my_data_tidy)
## participant sex conservatorio c_nat
## Min. : 1.00 Length:180 Length:180 Min. : 2.000
## 1st Qu.: 45.75 Class :character Class :character 1st Qu.: 6.000
## Median : 90.50 Mode :character Mode :character Median : 7.000
## Mean : 90.49 Mean : 7.106
## 3rd Qu.:135.25 3rd Qu.: 8.000
## Max. :180.00 Max. :10.000
## c_soc ef_fi leng_cas leng_cat
## Min. : 2.000 Min. : 4.000 Min. : 3.000 Min. : 3.000
## 1st Qu.: 6.000 1st Qu.: 7.000 1st Qu.: 6.000 1st Qu.: 6.000
## Median : 8.000 Median : 8.000 Median : 7.000 Median : 7.000
## Mean : 7.439 Mean : 8.039 Mean : 6.911 Mean : 6.794
## 3rd Qu.: 9.000 3rd Qu.: 9.000 3rd Qu.: 8.000 3rd Qu.: 8.000
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
## leng_est mat plast tecn
## Min. : 3.000 Min. : 1.000 Min. : 4.000 Min. : 3.000
## 1st Qu.: 6.000 1st Qu.: 5.000 1st Qu.: 7.000 1st Qu.: 6.000
## Median : 8.000 Median : 7.000 Median : 8.000 Median : 8.000
## Mean : 7.544 Mean : 6.828 Mean : 7.739 Mean : 7.483
## 3rd Qu.: 9.000 3rd Qu.: 9.000 3rd Qu.: 9.000 3rd Qu.: 9.000
## Max. :10.000 Max. :10.000 Max. :10.000 Max. :10.000
my_data_tidy_proyecto %>%
group_by(., conservatorio) %>%
summarize(., mean_c_nat = mean(c_nat), sd_c_nat = sd(c_nat),
mean_c_soc = mean(c_soc), sd_c_soc = sd(c_soc),
mean_ef_fi = mean(ef_fi), sd_ef_fi = sd(ef_fi),
mean_leng_cas = mean(leng_cas), sd_leng_cas = sd(leng_cas),
mean_leng_cat = mean(leng_cat), sd_leng_cat = sd(leng_cat),
mean_leng_est = mean(leng_est), sd_leng_est = sd(leng_est),
mean_mat = mean(mat), sd_mat = sd(mat),
mean_plast = mean(plast), sd_plast = sd(plast),
mean_tecn = mean(tecn), sd_tecn = sd(tecn))
## # A tibble: 2 × 19
## conservatorio mean_c…¹ sd_c_…² mean_…³ sd_c_…⁴ mean_…⁵ sd_ef…⁶ mean_…⁷ sd_le…⁸
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 no 6.39 1.68 7.32 1.80 7.92 1.36 6.42 1.49
## 2 si 7.82 1.40 7.56 1.51 8.16 1.18 7.4 1.50
## # … with 10 more variables: mean_leng_cat <dbl>, sd_leng_cat <dbl>,
## # mean_leng_est <dbl>, sd_leng_est <dbl>, mean_mat <dbl>, sd_mat <dbl>,
## # mean_plast <dbl>, sd_plast <dbl>, mean_tecn <dbl>, sd_tecn <dbl>, and
## # abbreviated variable names ¹mean_c_nat, ²sd_c_nat, ³mean_c_soc, ⁴sd_c_soc,
## # ⁵mean_ef_fi, ⁶sd_ef_fi, ⁷mean_leng_cas, ⁸sd_leng_cas
knitr::kable(my_data_tidy_proyecto %>%
group_by(., conservatorio, sex) %>%
summarize(., mean_c_nat = mean(c_nat), sd_c_nat = sd(c_nat),
mean_c_soc = mean(c_soc), sd_c_soc = sd(c_soc),
mean_ef_fi = mean(ef_fi), sd_ef_fi = sd(ef_fi),
mean_leng_cas = mean(leng_cas), sd_leng_cas = sd(leng_cas),
mean_leng_cat = mean(leng_cat), sd_leng_cat = sd(leng_cat),
mean_leng_est = mean(leng_est), sd_leng_est = sd(leng_est),
mean_mat = mean(mat), sd_mat = sd(mat),
mean_plast = mean(plast), sd_plast = sd(plast),
mean_tecn = mean(tecn), sd_tecn = sd(tecn)))
## `summarise()` has grouped output by 'conservatorio'. You can override using the
## `.groups` argument.
| no |
female |
6.088889 |
1.635157 |
7.000000 |
1.651446 |
7.888889 |
1.569919 |
6.111111 |
1.480513 |
5.977778 |
1.514909 |
7.066667 |
1.737291 |
5.533333 |
2.051607 |
6.955556 |
1.476414 |
6.577778 |
1.514909 |
| no |
male |
6.688889 |
1.689839 |
7.644444 |
1.896834 |
7.955556 |
1.127256 |
6.733333 |
1.452271 |
6.444444 |
1.574737 |
7.600000 |
1.875682 |
6.244444 |
1.967411 |
7.600000 |
1.543314 |
7.222222 |
1.490712 |
| si |
female |
7.777778 |
1.222681 |
7.644444 |
1.539710 |
8.022222 |
1.157758 |
7.422222 |
1.469213 |
7.377778 |
1.434777 |
7.777778 |
1.636083 |
7.622222 |
1.613704 |
8.177778 |
1.211477 |
8.066667 |
1.268499 |
| si |
male |
7.866667 |
1.575379 |
7.466667 |
1.501514 |
8.288889 |
1.198905 |
7.377778 |
1.541677 |
7.377778 |
1.511906 |
7.733333 |
1.601136 |
7.911111 |
1.794211 |
8.222222 |
1.020002 |
8.066667 |
1.250455 |
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = c_nat, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = c_soc, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = ef_fi, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = leng_cas, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = leng_cat, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = leng_est, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = mat, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = plast, color = sex)) +
geom_boxplot()
my_data_tidy_proyecto %>%
ggplot(aes(x = conservatorio, y = tecn, color = sex)) +
geom_boxplot()
mod_null_c_nat <- lm(c_nat ~ 1, data = my_data_tidy_proyecto)
mod1_c_nat <- lm(c_nat ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_c_nat <- lm(c_nat ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_c_nat <- lm(c_nat ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_c_nat)
##
## Call:
## lm(formula = c_nat ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8667 -0.8667 0.1333 1.2222 2.9111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0889 0.2298 26.496 < 2e-16 ***
## conservatoriosi 1.6889 0.3250 5.197 5.58e-07 ***
## sexmale 0.6000 0.3250 1.846 0.0665 .
## conservatoriosi:sexmale -0.5111 0.4596 -1.112 0.2676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.542 on 176 degrees of freedom
## Multiple R-squared: 0.1941, Adjusted R-squared: 0.1803
## F-statistic: 14.13 on 3 and 176 DF, p-value: 2.716e-08
anova(mod_null_c_nat, mod1_c_nat, mod2_c_nat, mod_int_c_nat)
## Analysis of Variance Table
##
## Model 1: c_nat ~ 1
## Model 2: c_nat ~ 1 + conservatorio
## Model 3: c_nat ~ 1 + conservatorio + sex
## Model 4: c_nat ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 518.99
## 2 178 426.54 1 92.450 38.9015 3.216e-09 ***
## 3 177 421.21 1 5.339 2.2465 0.1357
## 4 176 418.27 1 2.939 1.2366 0.2676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_c_nat)
durbinWatsonTest(mod_int_c_nat)
## lag Autocorrelation D-W Statistic p-value
## 1 0.04504658 1.878689 0.368
## Alternative hypothesis: rho != 0
mod_null_c_soc <- lm(c_soc ~ 1, data = my_data_tidy_proyecto)
mod1_c_soc <- lm(c_soc ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_c_soc <- lm(c_soc ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_c_soc <- lm(c_soc ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_c_soc)
##
## Call:
## lm(formula = c_soc ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6444 -1.4667 0.3556 1.3556 3.0000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.0000 0.2466 28.380 <2e-16 ***
## conservatoriosi 0.6444 0.3488 1.848 0.0664 .
## sexmale 0.6444 0.3488 1.848 0.0664 .
## conservatoriosi:sexmale -0.8222 0.4933 -1.667 0.0973 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.655 on 176 degrees of freedom
## Multiple R-squared: 0.0253, Adjusted R-squared: 0.008684
## F-statistic: 1.523 on 3 and 176 DF, p-value: 0.2103
anova(mod_null_c_soc, mod1_c_soc, mod2_c_soc, mod_int_c_soc)
## Analysis of Variance Table
##
## Model 1: c_soc ~ 1
## Model 2: c_soc ~ 1 + conservatorio
## Model 3: c_soc ~ 1 + conservatorio + sex
## Model 4: c_soc ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 494.33
## 2 178 491.88 1 2.4500 0.8949 0.34544
## 3 177 489.43 1 2.4500 0.8949 0.34544
## 4 176 481.82 1 7.6056 2.7782 0.09734 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_c_soc)
durbinWatsonTest(mod_int_c_soc)
## lag Autocorrelation D-W Statistic p-value
## 1 0.06295135 1.864671 0.316
## Alternative hypothesis: rho != 0
mod_null_ef_fi <- lm(ef_fi ~ 1, data = my_data_tidy_proyecto)
mod1_ef_fi <- lm(ef_fi ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_ef_fi <- lm(ef_fi ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_ef_fi <- lm(ef_fi ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_ef_fi)
##
## Call:
## lm(formula = ef_fi ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8889 -0.9556 0.0444 1.0444 2.1111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.88889 0.19022 41.472 <2e-16 ***
## conservatoriosi 0.13333 0.26901 0.496 0.621
## sexmale 0.06667 0.26901 0.248 0.805
## conservatoriosi:sexmale 0.20000 0.38044 0.526 0.600
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.276 on 176 degrees of freedom
## Multiple R-squared: 0.01427, Adjusted R-squared: -0.002528
## F-statistic: 0.8496 on 3 and 176 DF, p-value: 0.4685
anova(mod_null_ef_fi, mod1_ef_fi, mod2_ef_fi, mod_int_ef_fi)
## Analysis of Variance Table
##
## Model 1: ef_fi ~ 1
## Model 2: ef_fi ~ 1 + conservatorio
## Model 3: ef_fi ~ 1 + conservatorio + sex
## Model 4: ef_fi ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 290.73
## 2 178 288.28 1 2.45 1.5047 0.2216
## 3 177 287.03 1 1.25 0.7677 0.3821
## 4 176 286.58 1 0.45 0.2764 0.5998
plot(mod_int_ef_fi)
durbinWatsonTest(mod_int_ef_fi)
## lag Autocorrelation D-W Statistic p-value
## 1 0.07146747 1.838989 0.224
## Alternative hypothesis: rho != 0
mod_null_leng_cas <- lm(leng_cas ~ 1, data = my_data_tidy_proyecto)
mod1_leng_cas <- lm(leng_cas ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_leng_cas <- lm(leng_cas ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_leng_cas <- lm(leng_cas ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_leng_cas)
##
## Call:
## lm(formula = leng_cas ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4222 -1.1111 -0.1111 0.9833 3.2667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1111 0.2216 27.582 < 2e-16 ***
## conservatoriosi 1.3111 0.3133 4.184 4.51e-05 ***
## sexmale 0.6222 0.3133 1.986 0.0486 *
## conservatoriosi:sexmale -0.6667 0.4431 -1.504 0.1343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.486 on 176 degrees of freedom
## Multiple R-squared: 0.1175, Adjusted R-squared: 0.1025
## F-statistic: 7.813 on 3 and 176 DF, p-value: 6.321e-05
anova(mod_null_leng_cas, mod1_leng_cas, mod2_leng_cas, mod_int_leng_cas)
## Analysis of Variance Table
##
## Model 1: leng_cas ~ 1
## Model 2: leng_cas ~ 1 + conservatorio
## Model 3: leng_cas ~ 1 + conservatorio + sex
## Model 4: leng_cas ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 440.58
## 2 178 397.56 1 43.022 19.4751 1.773e-05 ***
## 3 177 393.80 1 3.756 1.7000 0.1940
## 4 176 388.80 1 5.000 2.2634 0.1343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_leng_cas)
durbinWatsonTest(mod_int_leng_cas)
## lag Autocorrelation D-W Statistic p-value
## 1 0.08570086 1.810181 0.178
## Alternative hypothesis: rho != 0
mod_null_leng_cat <- lm(leng_cat ~ 1, data = my_data_tidy_proyecto)
mod1_leng_cat <- lm(leng_cat ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_leng_cat <- lm(leng_cat ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_leng_cat <- lm(leng_cat ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_leng_cat)
##
## Call:
## lm(formula = leng_cat ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3778 -1.3778 0.0222 1.0222 3.5556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.9778 0.2251 26.558 < 2e-16 ***
## conservatoriosi 1.4000 0.3183 4.398 1.89e-05 ***
## sexmale 0.4667 0.3183 1.466 0.144
## conservatoriosi:sexmale -0.4667 0.4502 -1.037 0.301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.51 on 176 degrees of freedom
## Multiple R-squared: 0.1415, Adjusted R-squared: 0.1269
## F-statistic: 9.672 on 3 and 176 DF, p-value: 6.08e-06
anova(mod_null_leng_cat, mod1_leng_cat, mod2_leng_cat, mod_int_leng_cat)
## Analysis of Variance Table
##
## Model 1: leng_cat ~ 1
## Model 2: leng_cat ~ 1 + conservatorio
## Model 3: leng_cat ~ 1 + conservatorio + sex
## Model 4: leng_cat ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 467.39
## 2 178 406.14 1 61.25 26.8664 5.935e-07 ***
## 3 177 403.69 1 2.45 1.0747 0.3013
## 4 176 401.24 1 2.45 1.0747 0.3013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_leng_cat)
durbinWatsonTest(mod_int_leng_cat)
## lag Autocorrelation D-W Statistic p-value
## 1 0.04466352 1.899911 0.438
## Alternative hypothesis: rho != 0
mod_null_leng_est <- lm(leng_est ~ 1, data = my_data_tidy_proyecto)
mod1_leng_est <- lm(leng_est ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_leng_est <- lm(leng_est ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_leng_est <- lm(leng_est ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_leng_est)
##
## Call:
## lm(formula = leng_est ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0667 -1.6000 0.2444 1.2667 2.9333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.0667 0.2558 27.627 <2e-16 ***
## conservatoriosi 0.7111 0.3617 1.966 0.0509 .
## sexmale 0.5333 0.3617 1.474 0.1422
## conservatoriosi:sexmale -0.5778 0.5116 -1.129 0.2603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.716 on 176 degrees of freedom
## Multiple R-squared: 0.02716, Adjusted R-squared: 0.01058
## F-statistic: 1.638 on 3 and 176 DF, p-value: 0.1823
anova(mod_null_leng_est, mod1_leng_est, mod2_leng_est, mod_int_leng_est)
## Analysis of Variance Table
##
## Model 1: leng_est ~ 1
## Model 2: leng_est ~ 1 + conservatorio
## Model 3: leng_est ~ 1 + conservatorio + sex
## Model 4: leng_est ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 532.64
## 2 178 524.62 1 8.0222 2.7248 0.1006
## 3 177 521.93 1 2.6889 0.9133 0.3406
## 4 176 518.18 1 3.7556 1.2756 0.2603
plot(mod_int_leng_est)
durbinWatsonTest(mod_int_leng_est)
## lag Autocorrelation D-W Statistic p-value
## 1 0.03697763 1.916163 0.524
## Alternative hypothesis: rho != 0
mod_null_mat <- lm(mat ~ 1, data = my_data_tidy_proyecto)
mod1_mat <- lm(mat ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_mat <- lm(mat ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_mat <- lm(mat ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_mat)
##
## Call:
## lm(formula = mat ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5333 -1.2444 0.0889 1.3778 3.7556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5333 0.2779 19.910 < 2e-16 ***
## conservatoriosi 2.0889 0.3930 5.315 3.2e-07 ***
## sexmale 0.7111 0.3930 1.809 0.0721 .
## conservatoriosi:sexmale -0.4222 0.5558 -0.760 0.4485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.864 on 176 degrees of freedom
## Multiple R-squared: 0.2194, Adjusted R-squared: 0.2061
## F-statistic: 16.49 on 3 and 176 DF, p-value: 1.737e-09
anova(mod_null_mat, mod1_mat, mod2_mat, mod_int_mat)
## Analysis of Variance Table
##
## Model 1: mat ~ 1
## Model 2: mat ~ 1 + conservatorio
## Model 3: mat ~ 1 + conservatorio + sex
## Model 4: mat ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 783.66
## 2 178 624.99 1 158.672 45.6511 1.99e-10 ***
## 3 177 613.74 1 11.250 3.2367 0.07372 .
## 4 176 611.73 1 2.006 0.5770 0.44850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_mat)
durbinWatsonTest(mod_int_mat)
## lag Autocorrelation D-W Statistic p-value
## 1 0.1154332 1.734837 0.068
## Alternative hypothesis: rho != 0
mod_null_plast <- lm(plast ~ 1, data = my_data_tidy_proyecto)
mod1_plast <- lm(plast ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_plast <- lm(plast ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_plast <- lm(plast ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_plast)
##
## Call:
## lm(formula = plast ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6000 -0.9556 -0.1778 0.8222 2.4000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.9556 0.1982 35.097 < 2e-16 ***
## conservatoriosi 1.2222 0.2803 4.361 2.2e-05 ***
## sexmale 0.6444 0.2803 2.299 0.0227 *
## conservatoriosi:sexmale -0.6000 0.3964 -1.514 0.1319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.329 on 176 degrees of freedom
## Multiple R-squared: 0.1329, Adjusted R-squared: 0.1181
## F-statistic: 8.989 on 3 and 176 DF, p-value: 1.429e-05
anova(mod_null_plast, mod1_plast, mod2_plast, mod_int_plast)
## Analysis of Variance Table
##
## Model 1: plast ~ 1
## Model 2: plast ~ 1 + conservatorio
## Model 3: plast ~ 1 + conservatorio + sex
## Model 4: plast ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 358.73
## 2 178 320.46 1 38.272 21.6542 6.4e-06 ***
## 3 177 315.12 1 5.339 3.0207 0.08396 .
## 4 176 311.07 1 4.050 2.2915 0.13188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_plast)
durbinWatsonTest(mod_int_plast)
## lag Autocorrelation D-W Statistic p-value
## 1 -0.09708371 2.171191 0.32
## Alternative hypothesis: rho != 0
mod_null_tecn <- lm(tecn ~ 1, data = my_data_tidy_proyecto)
mod1_tecn <- lm(tecn ~ 1 + conservatorio, data = my_data_tidy_proyecto)
mod2_tecn <- lm(tecn ~ 1 + conservatorio + sex, data = my_data_tidy_proyecto)
mod_int_tecn <- lm(tecn ~ 1 + conservatorio + sex + conservatorio:sex, data = my_data_tidy_proyecto)
summary(mod_int_tecn)
##
## Call:
## lm(formula = tecn ~ 1 + conservatorio + sex + conservatorio:sex,
## data = my_data_tidy_proyecto)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5778 -1.0667 -0.0667 0.9333 2.7778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.5778 0.2067 31.824 < 2e-16 ***
## conservatoriosi 1.4889 0.2923 5.094 8.99e-07 ***
## sexmale 0.6444 0.2923 2.205 0.0288 *
## conservatoriosi:sexmale -0.6444 0.4134 -1.559 0.1208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.387 on 176 degrees of freedom
## Multiple R-squared: 0.1726, Adjusted R-squared: 0.1585
## F-statistic: 12.24 on 3 and 176 DF, p-value: 2.595e-07
anova(mod_null_tecn, mod1_tecn, mod2_tecn, mod_int_tecn)
## Analysis of Variance Table
##
## Model 1: tecn ~ 1
## Model 2: tecn ~ 1 + conservatorio
## Model 3: tecn ~ 1 + conservatorio + sex
## Model 4: tecn ~ 1 + conservatorio + sex + conservatorio:sex
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 179 408.95
## 2 178 347.70 1 61.250 31.8600 6.524e-08 ***
## 3 177 343.03 1 4.672 2.4303 0.1208
## 4 176 338.36 1 4.672 2.4303 0.1208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mod_int_tecn)
durbinWatsonTest(mod_int_tecn)
## lag Autocorrelation D-W Statistic p-value
## 1 0.2065035 1.57429 0.004
## Alternative hypothesis: rho != 0